Options for improved livestock activity data collection and management to support measurement, reporting and verification (MRV) in Ethiopia

To complement an ongoing CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) project ‘Enhancing capacities for MRV of sustainable livestock action in East Africa (Kenya and Ethiopia)’, which is implemented by UNIQUE forestry and land use, the Australian Centre for International Agricultural Research (ACIAR) supported CCAFS to implement a Small Research Activity (SRA) entitled ‘Building capacities for an integrated livestock MRV system in Ethiopia’. The objective of the SRA was to support Ethiopian stakeholders to improve the methods and procedures used to produce and manage the livestock activity data required for measurement, reporting and verification (MRV) of greenhouse gases (GHG) in Ethiopia. This report summarizes the main results of the project’s activities and the activity data collection and data management methods recommended for use in Ethiopia. These findings may be relevant to livestock MRV in other countries in East Africa and elsewhere. Section 1 explains the focus of the research activities on livestock activity data in Ethiopia. Ethiopia has identified livestock as a key subsector for GHG mitigation. An advanced livestock GHG inventory has been compiled using the Tier 2 method. However, some gaps remain in terms of data availability and data quality. The pilot activities were designed to fill those gaps, and thus enable Ethiopia to better quantify livestock GHG emissions and emission reductions in line with its national commitments on climate change. Section 2 describes the data collection tools and data management methods tested and the results. The data collection tools focused on collecting improved data on diet composition, milk yield and manure management activity data. A further pilot involved the Ethiopian Institute for Agricultural Research (EIAR) in analyses and communication of data. Section 3 highlights key recommendations for the adoption of the tools and data management activities evaluated by stakeholders. It also provides some reflections on the piloting process from the project team, which may provide some guidance for future piloting of MRV innovations elsewhere in East Africa. Section 4 presents the manual’s purpose for each production system to guide data collection procedures. It extends to guidance and pilots’ lessons learned on sampling, selection of participants, preliminary analysis, and other practical implementation issues. These enable scaling up of the use of the piloted tools to regional and national levels

Methods and guidance to support MRV of livestock emissions: Methods for data collection, analysis and summary results from a pilot baseline survey for the Kenya dairy NAMA

There is increasing interest in mitigation of greenhouse gas (GHG) emissions from the dairy sector in developing countries. However, there is little prior experience with measurement, reporting and verification (MRV) of GHG emissions and emission reductions. A voluntary carbon market methodology, the Smallholder Dairy Methodology, has proposed a methodology for establishing a standardized performance baseline for a region targeted by a GHG mitigation initiative. This working paper reports the first experience of implementing a survey and analyzing survey data to establish a standardized performance baseline using survey data from central Kenya, which is a region targeted by the Kenya dairy NAMA promoted by the Government of Kenya. The publication of this report enables transparent documentation of the baseline setting process for the Kenya dairy NAMA. Data from the survey were also used to characterize dairy production in the intensive production system in Kenya’s Tier 2 GHG inventory for dairy cattle. Publication of the survey data also supports transparency of Kenya’s Tier 2 GHG inventory. The report summarizes the requirements of the Smallholder Dairy Methodology, the methods used for sampling, data collection and data analysis, the main results of data analysis and recommendations for future similar initiatives to quantify standardized baselines for dairy GHG mitigation programs. Appendices present data collection tools, summary statistics, and the data used to estimate parameters in Kenya’s Tier 2 dairy GHG inventory. Analysis of the survey data following the Smallholder Dairy Methodology’s requirements shows that the relationship between GHG intensity (kg CO2e/kg fat and protein corrected milk [FPCM]) and milk yield (kg FPCM per farm per year) can be represented by a power regression: y = 81.868x-0.436. Using this relationship, dairy initiatives in central Kenya need only to measure change in milk yield per farm per year, and can estimate GHG emissions and emission reductions using the relationship published here. The regression has an r2 of 0.43, and an uncertainty of 18.6% as measured by the root mean square error (RMSE) of the regression. The Smallholder Dairy Methodology does not require quantification of uncertainty, but other mitigation initiatives may use estimated uncertainty to discount the GHG emission reductions claimed in order to ensure conservativeness. The baseline survey is representative of 8 counties with a dairy cattle population of about 1.7 million, and data collection and analysis cost about US$ 75,000. The methodology is therefore a cost-effective way to set baselines for an initiative with large numbers of participating farms

Inventory of greenhouse gas emissions from cattle, sheep and goats in Ethiopia (1994-2018) calculated using the IPCC Tier 2 approach

Livestock production is an important source of incomes and livelihoods for the rural population in Ethiopia, and is also an important contributor to greenhouse gas (GHG) emissions. Ethiopia’s GHG profile is dominated by emissions from the agriculture sector, representing 79% of total national emissions. Of these emissions, livestock accounts for the largest share (60%) due to enteric fermentation, manure management and emissions from managed soils due to livestock deposit of dung and urine

Identifying therapeutic targets by combining transcriptional data with ordinal clinical measurements

The immense and growing repositories of transcriptional data may contain critical insights for developing new therapies. Current approaches to mining these data largely rely on binary classifications of disease vs. control, and are not able to incorporate measures of disease severity. We report an analytical approach to integrate ordinal clinical information with transcriptomics. We apply this method to public data for a large cohort of Huntington's disease patients and controls, identifying and prioritizing phenotype-associated genes. We verify the role of a high-ranked gene in dysregulation of sphingolipid metabolism in the disease and demonstrate that inhibiting the enzyme, sphingosine-1-phosphate lyase 1 (SPL), has neuroprotective effects in Huntington's disease models. Finally, we show that one consequence of inhibiting SPL is intracellular inhibition of histone deacetylases, thus linking our observations in sphingolipid metabolism to a well-characterized Huntington's disease pathway. Our approach is easily applied to any data with ordinal clinical measurements, and may deepen our understanding of disease processes

Chronic myelomonocytic leukemia in younger patients : molecular and cytogenetic predictors of survival and treatment outcome

In patients with chronic myelomonocytic leukemia (CMML), age>65 years is an adverse prognostic factor. Our objective in the current study was to examine risk factors for survival and treatment outcome in 261 'young' adults with CMML, as defined by age \u2a7d65 years. In multivariable analysis, lower HB (P=0.01), higher circulating blast % (P=0.002), ASXL1 (P=0.0007) and SRSF2 mutations (P=0.008) and Mayo-French cytogenetic stratification (P=0.04) negatively impacted survival. Similarly, leukemia-free survival was independently affected by higher circulating blast % (P<0.0001), higher bone marrow blast % (P=0.0007) and the presence of circulating immature myeloid cells (P=0.0002). Seventy-five (29%) patients received hypomethylating agents (HMA), with the median number of cycles being 5, and the median duration of therapy being 5 months. The over-all response rate was 40% for azacitidine and 30% for decitabine. Fifty-three (24%) patients underwent an allogeneic hematopoietic stem cell transplant (AHSCT), with a response rate of 56% and a non-relapse mortality of 19%. Survival in young adults with CMML, although higher than in older patients, is poor and even worse in the presence of ASXL1 and SRSF2 mutations. Treatment outcome was more impressive with AHSCT than with HMA and neither was influenced by ASXL1/SRSF2 mutations or karyotype

High seroprevalence of anti-SARS-CoV-2 antibodies among Ethiopian healthcare workers

BACKGROUND: COVID-19 pandemic has a devastating impact on the economies and health care system of sub-Saharan Africa. Healthcare workers (HWs), the main actors of the health system, are at higher risk because of their occupation. Serology-based estimates of SARS-CoV-2 infection among HWs represent a measure of HWs' exposure to the virus and could be used as a guide to the prevalence of SARS-CoV-2 in the community and valuable in combating COVID-19. This information is currently lacking in Ethiopia and other African countries. This study aimed to develop an in-house antibody testing assay, assess the prevalence of SARS-CoV-2 antibodies among Ethiopian high-risk frontline HWs. METHODS: We developed and validated an in-house Enzyme-Linked Immunosorbent Assay (ELISA) for specific detection of anti-SARS-CoV-2 receptor binding domain immunoglobin G (IgG) antibodies. We then used this assay to assess the seroprevalence among HWs in five public hospitals located in different geographic regions of Ethiopia. From consenting HWs, blood samples were collected between December 2020 and February 2021, the period between the two peaks of COVID-19 in Ethiopia. Socio-demographic and clinical data were collected using questionnaire-based interviews. Descriptive statistics and bivariate and multivariate logistic regression were used to determine the overall and post-stratified seroprevalence and the association between seropositivity and potential risk factors. RESULTS: Our successfully developed in-house assay sensitivity was 100% in serum samples collected 2- weeks after the first onset of symptoms whereas its specificity in pre-COVID-19 pandemic sera was 97.7%. Using this assay, we analyzed a total of 1997 sera collected from HWs. Of 1997 HWs who provided a blood sample, and demographic and clinical data, 51.7% were females, 74.0% had no symptoms compatible with COVID-19, and 29.0% had a history of contact with suspected or confirmed patients with SARS-CoV-2 infection. The overall seroprevalence was 39.6%. The lowest (24.5%) and the highest (48.0%) seroprevalence rates were found in Hiwot Fana Specialized Hospital in Harar and ALERT Hospital in Addis Ababa, respectively. Of the 821 seropositive HWs, 224(27.3%) of them had a history of symptoms consistent with COVID-19 while 436 (> 53%) of them had no contact with COVID-19 cases as well as no history of COVID-19 like symptoms. A history of close contact with suspected/confirmed COVID-19 cases is associated with seropositivity (Adjusted Odds Ratio (AOR) = 1.4, 95% CI 1.1-1.8; p = 0.015). CONCLUSION: High SARS-CoV-2 seroprevalence levels were observed in the five Ethiopian hospitals. These findings highlight the significant burden of asymptomatic infection in Ethiopia and may reflect the scale of transmission in the general population

Modulation of Transcriptional and Inflammatory Responses in Murine Macrophages by the Mycobacterium tuberculosis Mammalian Cell Entry (Mce) 1 Complex

The outcome of many infections depends on the initial interactions between agent and host. Aiming at elucidating the effect of the M. tuberculosis Mce1 protein complex on host transcriptional and immunological responses to infection with M. tuberculosis, RNA from murine macrophages at 15, 30, 60 min, 4 and 10 hrs post-infection with M. tuberculosis H37Rv or Δ-mce1 H37Rv was analyzed by whole-genome microarrays and RT-QPCR. Immunological responses were measured using a 23-plex cytokine assay. Compared to uninfected controls, 524 versus 64 genes were up-regulated by 15 min post H37Rv- and Δ-mce1 H37Rv-infection, respectively. By 15 min post-H37Rv infection, a decline of 17 cytokines combined with up-regulation of Ccl24 (26.5-fold), Clec4a2 (23.2-fold) and Pparγ (10.5-fold) indicated an anti-inflammatory response initiated by IL-13. Down-regulation of Il13ra1 combined with up-regulation of Il12b (30.2-fold), suggested switch to a pro-inflammatory response by 4 hrs post H37Rv-infection. Whereas no significant change in cytokine concentration or transcription was observed during the first hour post Δ-mce1 H37Rv-infection, a significant decline of IL-1b, IL-9, IL-13, Eotaxin and GM-CSF combined with increased transcription of Il12b (25.1-fold) and Inb1 (17.9-fold) by 4 hrs, indicated a pro-inflammatory response. The balance between pro-and anti-inflammatory responses during the early stages of infection may have significant bearing on outcome